Frames of reference for perceiving motion direction in the human visual system
Bibliographic record
Abstract
The problem of reference frame coding for visual-to-motor transformations has been widely investigated using a variety of stationary stimuli and visuomotor tasks. Here we investigate the reference frame(s) for motion direction perception in human subjects. We recorded behavioral responses in 4 subjects while they performed a direction ‘perception’ + saccade (experiment 1) and a direction discrimination + key-press-2AFC (experiment 2) tasks. In experiment 1 a random dot pattern (RDP) moving in one of 10 possible directions (from 0[deg] in steps of 10[deg] to 90[deg]) was presented to the subjects during 400ms. After stimulus offset, subjects rotated their heads 30[deg] to the left around the anterior posterior axis and made a saccade in the perceived direction. When comparing to a control condition, in which the head remained stationary, we found that saccade trajectories, mean saccade landing positions and saccade directional errors were not affected by head rotations. In experiment 2 subjects compared the direction of two RDPs presented during 400ms each and separated by a 2000ms time interval. We obtained psychometric curves in two conditions, a) when subjects kept the head straight during the 2000ms interval, and b) when subjects rotated the head 300 to the left during that interval. We found that on average the curves point of subjective equality in the latter condition was slightly displaced in the direction of the head tilt (from −1.78[deg] with the head straight to -2.76[deg] with the head rotated). However, this displacement was considerably smaller than the one predicted by a head/eye-centered model of motion direction perception and much closer to the predictions of a body/space centered model. These results argue against the hypothesis that visual motion is encoded in a head/eye-centered frame of reference. On the other hand, they suggest that motion direction is encoded in a body/space-centered reference frame.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".